Update model.py

model.py

@@ -42,12 +42,9 @@ def smart_summarize(text, n_clusters=1):
         idx = np.where(kmeans.labels_ == i)[0]
         if not len(idx):
             continue
-        # Average vector from cluster, converted to ndarray
-        avg_vector = tfidf_matrix[idx].mean(axis=0).A  # Convert matrix to ndarray
-        # Compute cosine similarity between avg_vector and tfidf vectors in cluster
+        avg_vector = np.asarray(tfidf_matrix[idx].mean(axis=0))
         sim = cosine_similarity(avg_vector, tfidf_matrix[idx].toarray())
         most_representative = sentences[idx[np.argmax(sim)]]
         summary_sentences.append(most_representative)
 
-    # Preserve original sentence order
     return " ".join(sorted(summary_sentences, key=sentences.index))